Extending the [C/N]-Age Calibration: Using Globular Clusters to Explore Older and Metal-Poor Populations
Bibliographic record
Abstract
In the coming years, detailed chemical abundances from large-scale high-resolution spectroscopic surveys will become available for vast numbers of stars across the Milky Way. Previous work has suggested that abundance ratios from these spectra can allow us to estimate ages from a large number of stars. These data will be leveraged to calibrate chemical clocks to age-date field stars, as reliable stellar ages remain elusive. In this work, we extended our empirical relationship between stellar age and their carbon-to-nitrogen ([C/N]) abundance ratio for evolved stars to older and more metal-poor stars by combining the original open cluster calibration sample and four globular clusters: 47 Tuc, M 71, M 4, and M 5. With this extension, [C/N] can be used as a chemical clock for evolved field stars to investigate not only regions within the metal rich disk, but also more metal-poor regions of our Galaxy. We have established the [C/N]-age relationship for APOGEE DR17 red giant stars, that have experienced the first dredge up but have not yet undergone any extra-mixing, in clusters usable for ages between $8.62 \leq \log(Age[{\rm yr}]) \leq 10.13$ and for metallicites of $-1.2\leq[Fe/H]\leq+0.3$. This relationship can be uniformly applied to these stars within the APOGEE DR17 sample. This measured [C/N]-age APOGEE DR17 relationship is also shown to be consistent with stellar ages derived from asterosiesmic results of APOKASC and APO-K2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".